Figure REF  shows the architecture of RELEVAGAN. The structure of RELEVAGAN has mainly two components. One is EVAGAN, summarised in Section REF , and the other part is a DRL-based model. For more details on EVAGAN, readers are highly encouraged to refer to the paper [1]}. A typical DRL model consisting of an agent and an environment has been coupled with EVAGAN architecture in RELEVAGAN design. Like other DRL-based attackers for evasion generation using a black box attack [2]}, [3]}, [4]}, [5]}, [6]}, our proposed DRL agent attacks the \(\mathcal {D}\)  of EVAGAN, which acts as a black box classifier. \(\mathcal {D}\) 's output for minority class estimation is used as the reward for the agent to adjust its weights and generate a new action \(a_{t+n}\)  based on some policy \(\pi \) . The new action is fed to the environment where the state generator creates a new state taking another seed sample from the real data set. As the result of a single iteration, the new state and the collected reward are fed back to the agent. As a result of the positive reward, the evasion samples are fed to the \(\mathcal {D}\)  of EVAGAN to adversarially train it. In this way, the \(\mathcal {D}\)  becomes proactively aware of any possible future evasions and ready to give better feedback to the \(\mathcal {G}\)  to train to confine its boundaries for evasion generation. The process leads to the early convergence of \(\mathcal {G}\)  training.
